AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation

Related tags

Deep LearningTAPG-AgentEnvInteration
Overview

AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation

A pytorch-version implementation codes of paper: "AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation", which is accepted in BMVC 2021.

Prerequisites

  • Python 3.8
  • Pytorch 1.9
  • fvcore
  • numpy, pandas, matplotlib, tensorboardX etc.

Download Features

3D Resnet-50 features extracted from rescaled videos of ActivityNet-1.3 can be downloaded [here](will be updated soon).

Training and Testing of AEI

Default configurations of AEI are stored in config/defaults.py. The modified configurations are stored in config/*.yaml for training and testing of AEI on different datasets (ActivityNet-1.3 and THUMOS-14). We can also modify configurations through commandline arguments.

  1. To train AEI on TAPG task of ActivityNet-1.3 with 1 GPU:
python main.py --cfg-file config/anet_proposals.yaml MODE 'training' GPU_IDS [0]
  1. To evaluate AEI on validation set of ActivityNet-1.3 with 1 GPU:
python main.py --cfg-file config/anet_proposals.yaml MODE 'validation' GPU_IDS [0]

Reference

This implementation is partly based on this pytorch-implementation of BMN.

paper:[AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation](will update soon)

Owner
PhD Student
GitHub Repository
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